Self-Supervised Neural Architecture Search for Imbalanced Datasets

TL;DR

This paper introduces a self-supervised neural architecture search framework tailored for imbalanced datasets, enabling resource-efficient model design without labels, demonstrated on medical and image datasets with superior performance and fewer parameters.

Contribution

It presents a novel NAS framework that operates in a self-supervised manner on imbalanced datasets, suitable for resource-constrained environments, extending recent self-supervised NAS methods.

Findings

Outperforms standard networks on imbalanced CIFAR-10 with 27x fewer parameters.

Successfully applied to ChestMNIST and COVID-19 X-ray datasets.

Can be run on a single GPU, demonstrating resource efficiency.

Abstract

Neural Architecture Search (NAS) provides state-of-the-art results when trained on well-curated datasets with annotated labels. However, annotating data or even having balanced number of samples can be a luxury for practitioners from different scientific fields, e.g., in the medical domain. To that end, we propose a NAS-based framework that bears the threefold contributions: (a) we focus on the self-supervised scenario, i.e., where no labels are required to determine the architecture, and (b) we assume the datasets are imbalanced, (c) we design each component to be able to run on a resource constrained setup, i.e., on a single GPU (e.g. Google Colab). Our components build on top of recent developments in self-supervised learning~\citep{zbontar2021barlow}, self-supervised NAS~\citep{kaplan2020self} and extend them for the case of imbalanced datasets. We conduct experiments on an (artificially) imbalanced version of CIFAR-10 and we demonstrate our proposed method outperforms standard neural networks, while using $27\times$ less parameters. To validate our assumption on a naturally imbalanced dataset, we also conduct experiments on ChestMNIST and COVID-19 X-ray. The results demonstrate how the proposed method can be used in imbalanced datasets, while it can be fully run on a single GPU. Code is available \href{https://github.com/TimofeevAlex/ssnas_imbalanced}{here}.